US2025315711A1PendingUtilityA1
Quantum machine learning method for multi-class classification
Est. expiryOct 6, 2043(~17.2 yrs left)· nominal 20-yr term from priority
G06N 10/60B82Y 10/00G06N 3/047G06N 3/0464G06N 10/20G06N 10/40
65
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Claims
Abstract
The present invention relates to a quantum machine learning method for multi-class classification, and the method comprises the steps of: applying a Quantum Convolution Neural Network (QCNN) quantum circuit to input data having q qubits, and outputting a feature vector based on Pauli-Z measurement; and applying a Quantum Neural Network (QNN) quantum circuit to the feature vector, and outputting a multi-class prediction vector with scalability increased compared to q qubits based on basis measurement.
Claims
exact text as granted — not AI-modified1 . A quantum machine learning method, the method comprising:
applying a Quantum Convolution Neural Network (QCNN) quantum circuit configured to input data having q qubits, and outputting a feature vector based on Pauli-Z measurement; and applying a Quantum Neural Network (QNN) quantum circuit to the feature vector, and outputting a multi-class prediction vector with greater scalability than q qubits based on basis measurement.
2 . The method of claim 1 , wherein the outputting a multi-class prediction vector inputs the feature vector into the QNN quantum circuit, and outputs a 2 q -dimensional observation value as a probability measurement for the multi-class prediction vector based on the basis measurement.
3 . The method of claim 1 , wherein the outputting a feature vector comprises:
receiving an image as the input data through a plurality of input channels as an input, and outputting an initial feature vector based on the Pauli-Z measurement; and receiving the initial feature vector as an input, and outputting a feature vector having q qubits for each input channel based on the Pauli-Z measurement.
4 . The method of claim 3 , further comprising: including one or more QCNN layers, and inputting an initial feature vector output by each QCNN layer based on the Pauli-Z measurement into a next QCNN layer.
5 . The method of claim 1 , wherein a probability amplitude regularizer is used as a loss function in learning the QNN quantum circuit, and a probability amplitude normalizer L PAR is configured to remove probability values for classes that are not used in learning the QNN quantum circuit, based on the following equation:
ℒ
P
A
R
(
Θ
;
X
)
=
-
∑
n
′
>
❘
"\[LeftBracketingBar]"
y
❘
"\[RightBracketingBar]"
2
q
log
(
1
-
p
n
′
)
,
q≥┌log 2 (|y|)┐, |y| is the number of classes of a task to be classified, 2 q is an output of QNN, Θ is trainable parameters, X is extracted features, and Pn′ is an n′-th projection matrix.
6 . The method of claim 5 , wherein a binary cross-entropy loss and a probability amplitude regularizer are used as loss functions in learning the QCNN quantum circuit and the QNN quantum circuit, and the binary cross-entropy loss L BCE is defined as shown in the following equation:
ℒ
BCE
(
Θ
;
X
)
=
-
∑
n
=
1
|
y
|
[
y
c
log
p
c
+
(
1
-
y
c
)
log
(
1
-
p
c
)
]
,
y c and p c
denote the one-hot encoded class label and the predicted probability, respectively.
7 . The method of claim 6 , wherein a train loss function for one-step single update is defined as shown in the following equation:
ℒ
(
Θ
;
ζ
)
=
1
❘
"\[LeftBracketingBar]"
ζ
❘
"\[RightBracketingBar]"
∑
(
X
,
y
)
∈
ζ
[
ℒ
BCE
(
Θ
;
X
)
+
ℒ
PAR
(
Θ
;
X
)
]
,
wherein ζ denotes a set of sampled mini-batch, consisting of the data X and the label y.Join the waitlist — get patent alerts
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